Smart ubiquitous computing systems have been developed and deployed in order to observe, monitor and track the state of various physical infrastructures, state physical objects, environment, human beings and their activities and utilize these observations to provide applications and services that enrich the lives of people and help them in their day-to-day activities. The environments in which such smart ubiquitous systems are deployed are referred to as “smart spaces”.
In general, smart spaces includes various categories of sensors adapted for sensing and observation of various parameters in the environment that may enable to perform analytics on them to alert the end-users about the consequence of changes in the state, if any. For example, sensors may be deployed to observe and track location of any physical object, observe weather conditions to monitor natural calamities, observe traffics on the road to enable traffic shaping and vehicle surveillance systems etc.
Observations as described above are made by sensors and increasingly more and more sensors will be embedded in physical objects and things in the smart spaces. These sensors have transducers that transform a real life event or phenomenon into an electrical signal or digital data. In addition the sensors have computation and networking capabilities. Increasingly many of these sensors will directly or indirectly be connected to the Internet. Many of the sensors will be deployed by organizations, companies or public sector entities such as city governments or utilities or government departments. Also, many of the sensors will actually be owned and operated by private individuals. In case of private individuals, sensors embedded in mobile phones used by individuals will be an important class of sensors.
A critical requirement for development of smart ubiquitous computing environments leading to development of “smart spaces” is the ability to collect data from a large set of diverse sensors, aggregate and store the sensor data, perform specialized analytics on the data and combine and correlate observations from multiple diverse and geographically dispersed sensors. There is a need for scalable computing platforms that are able to provide these capabilities to software developers, including third party software developers, who can use the sensor data and the derived analytics to create new novel applications. Also, such platforms may be made available as web services accessed over the Internet, In such cases, these platforms can be categorized under the class of cloud computing services referred to as Platform-as-a-Service (PaaS).
In the background art, several systems have been implemented that perform the task of analysis of data captured by different category of sensors or telecommunication devices having sensing capabilities which are deployed in any smart space environment. These systems incorporate sensor devices that sense the state of various physical entities in any smart-spaces environments that could be processed and analyzed further to monitor, administer and control the services catered through these smart-spaces remotely. Though, there has been efforts made in the past for real-time data capture and analysis thereof meant for remote smart-space monitoring, the need for a unified platform that integrates the suite of services capable of provisioning the development of real-time applications and management thereof from sensor data captured through any sensor device still exists in the art.
As of today, there are various PaaS available including Google App Engine, Heroku, and Microsoft Azure etc. However, these are limited to general purpose application development and therefore do not provide specific support for development, deployment and management of sensor-based applications. These platforms do not provide specialized services required in IOT/Cyber Physical Systems domain. In this domain, there is a need for specialized services to cater to applications that leverages web connected sensors and sensors available as part of smart mobile devices. Sensor discovery, description, interfacing, query and tasking are some of the key requirements. Additionally, the sensor driven applications need to be event driven and therefore require capabilities such as event processing or stream processing. Further, these domains may require support for various types of databases such as RDBMS, NOSQL and Object Stores, etc., for scalable storage of different types of sensor observations. Also, the diversified domains may require specialized analytics and data visualization for deriving inferences and value addition. None of the above disclosed PaaS platforms provide support for all these features in a single platform.
On the other hand, there are some sensor platforms available as cloud computing services such as Pachube (Cosm), Sun Microsystem Sensor Networks etc. However these platforms mainly focus on sensor data publishing, subscription and storage services with very elementary support for application development. Additionally, there is very little support in these platforms for location based processing, spatial and spatio-temporal processing. Additionally, these sensor platforms provide no support for crowd sourced applications to be developed and deployed on these platforms.
Further, there are some sensors and gateway device vendors in the market including companies such as Digi, Mobile Devices etc. who provide a cloud based web services for remote device monitoring, management and data acquisition. However, these services cater for sensors and devices from a particular vendor only and are therefore not suitable for multi-vendor generic sensor device management, data capture and observation processing. Additionally, these services have very limited support for sensor data storage and analytics and almost no support for application development and deployment.
Additionally, a behavior based machine-to-machine (M2M) platform is known in the art that facilitates communication with global sensor network to enable sensor device management and generate composite applications without direct programming. Another implementation facilitating sensor-device management in the art uses cross APIs for accessing the sensor data across different platforms in a real-time. Further, an activity management system particular to specific domain such as semiconductor manufacturing is known in the art that comprises the steps of data collection, data storage and activation of services enables for improving the operational efficiency of the semiconductor manufacturing plant. An architecture facilitating automatic generation of software code for development of sensor driven applications is disclosed in the art.
Further, a framework facilitating context-aware advertising is known in the art, wherein the framework delivers relevant contents/ads to the end-consumer in context with the consumers behavior/habits tracked through sensors deployed in a smart-space environment. Further, an application scope management platform is known that works on the aspects of crowd sensing adapted for web-application deployment and management thereof. An enterprise resource management analytics platform enables data integration from remote resources to facilitate remote surveillance, monitoring and real-time events of agencies, organizations and communities to ensure safety and security in their campuses. Further, a system implementing graph pattern query to simplify writing Stream Processing application by application developer is known. Further, systems facilitating efficient resource management in general for processing tasks in virtualized environment are known that utilizes sharing of resources for effective task management.
However, none of the existing systems, methods, platforms or frameworks provide a unified system that facilitates sensor driven distributed application development, testing, deployment, application life cycle management, analytics service, data storage service, sensor services and modeling and simulation for analytics. Also, existing systems lack comprehensive hosting of services such as sensor service, analytics service, identity & access control service, data storage service that are required for prompt and speed-up sensor application development. Further, none of the platforms disclosed in the art facilitates real-time development and deployment of sensor-based applications using a rich suite of services that enables sensor data reusability, data normalizing and data privacy. As most of the platforms lack generic capabilities of sensor data processing, this further leads to increase in costs and effort required for development and deployment of sensor based applications. Further, since the platforms are designed with specific to particular devices thereby bounded with security and privacy policies, there is a little scope of further application developments using third-party resources.
In the background art, there have been efforts made in the past for providing vehicle telemetry applications that enables intelligent transportation services to end-user subscribers. In general, these applications are either provided vertically by the vehicle manufacturers/OEMs etc. or made available to the driver's Smartphone. In both cases, the applications development is enabled by using sensor data from various vehicle on-board/off-board sensors such as GPS, accelerometer and the like. Further, there have been efforts made in the art for implementing cloud computing technologies in the vehicle for providing vehicle telemetry applications. Further, there are vehicle to vehicle ad-hoc networks (VANETs) available in the art facilitating the provision of vehicle telemetry applications in a specific transport domain. However, the need for a single unified platform facilitating an intelligent transportation system by way of providing intelligent transport services in the platform for develop, test and deploy various telemetry applications using these services still exists in the art.
Thus, in view of the above, there is a long-felt need in the industry to address the aforementioned deficiencies and inadequacies.